class_docstr: The TextEnvironment enables interaction of a LLM with an environment using tools.
docstr: Generate responses for a list of histories.
docstr_tok: ["Generate","responses","for","a","list","of","histories","."]
code: def generate(self, histories):
      """
      Generate responses for a list of histories.
      """
      active_histories = [
          i
          for i, history in enumerate(histories)
          if not history.completed
      ]
  
      query_tensors = [histories[i].tokens for i in active_histories]
      response_tensors = self._generate_batched(query_tensors)
      response_texts = self.tokenizer.batch_decode(response_tensors)
  
      for i, response_text, response_tensor in zip(
          active_histories, response_texts, response_tensors
      ):
          histories[i].append_segment(
              response_text, response_tensor, system=False
          )
  
      return histories
  
imports: import re
  import warnings
  import torch
  from accelerate.utils import extract_model_from_parallel
  from transformers import StoppingCriteria, StoppingCriteriaList
  from ..import_utils import is_rich_available
label_desc: Use image node_id 6 for calling the TextEnvironment obj's underlying member method code with example usage: obj.generate(histories) and returns: histories